Information technology is now routinely used by many enterprises to receive, process, and provide information via widely accessible electronic communications networks, such as the Internet. Yet most information technology systems will begin to deny service, or fail to process message traffic efficiently, when communications traffic exceeds a processing capacity of the system. Such failures in communication can significantly impair the operations of an enterprise in many ways. Slower website performance is also known to cause users/visitors to leave the website sooner. Another consequence of poor performance is that the website may be downgraded in search engine results rankings.
In recent years, enterprises and developers have sought an easy and affordable way to use cloud computing as a way to load and performance test their web-based applications. Cloud computing gets its name from the fact that the machine, storage, and application resources exist on a “cloud” of servers. In cloud computing shared resources, software and information are provided on-demand, like a public utility, via the Internet. Cloud computing is closely related to grid computing, which refers to the concept of interconnecting networked computers such that processing power, memory and data storage are all community resources that authorized users can utilize for specific tasks.
Load testing a web-based application or website can involve simulating a very large number (e.g., up to or beyond 1,000,000) of virtual website users via Hypertext Transfer Protocol (HTTP) or HTTP Secure (HTTPS) message intercommunications with the target website. For very large tests, sending and aggregating the test results data generated from all of the load servers to a database available to a dashboard in real-time has been problematic. The huge overhead of receiving and processing a very large number of Hi IP messages containing all of the requests and responses sent from each of the many load servers to the analytic servers responsible for analyzing the test results data can easily overwhelm the resources of the server.
Enterprises are also interested in real user measurement (RUM) data analysis that captures and collects data about present, real user experiences when actual users visit and use a website or web application. Traditional analytical tools have been able to provide data analysis solutions that collect data about past events, it has been problematic to deliver real-time business intelligence information based on actual mobile and desktop user experience as it occurs in the present. That is, traditional solutions have not been able to test, monitor, and measure real user behavior to gain the analytics and intelligence needed to capture performance metrics such as bandwidth and web page load time, as well as correlate the impact of such metrics on the behavior of users as it relates to the business' bottom line.